{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: tqdm in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from -r requirements.txt (line 1))\n",
      "Collecting matplotlib>=1.5.1 (from -r requirements.txt (line 2))\n",
      "  Downloading matplotlib-2.1.0-cp27-cp27mu-manylinux1_x86_64.whl (14.9MB)\n",
      "Requirement already up-to-date: numpy>=1.11.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from -r requirements.txt (line 3))\n",
      "Collecting scikit-learn>=0.18 (from -r requirements.txt (line 4))\n",
      "  Downloading scikit_learn-0.19.1-cp27-cp27mu-manylinux1_x86_64.whl (12.2MB)\n",
      "Collecting scipy>=0.18.0 (from -r requirements.txt (line 5))\n",
      "  Downloading scipy-1.0.0-cp27-cp27mu-manylinux1_x86_64.whl (46.7MB)\n",
      "Requirement already up-to-date: seaborn>=0.7.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from -r requirements.txt (line 6))\n",
      "Requirement already up-to-date: six>=1.10.0 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from -r requirements.txt (line 7))\n",
      "Collecting autograd>=1.1.7 (from -r requirements.txt (line 8))\n",
      "  Downloading autograd-1.2.tar.gz\n",
      "Requirement already up-to-date: subprocess32 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "Requirement already up-to-date: cycler>=0.10 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "Collecting backports.functools-lru-cache (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "  Downloading backports.functools_lru_cache-1.4-py2.py3-none-any.whl\n",
      "Collecting pytz (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "  Downloading pytz-2017.3-py2.py3-none-any.whl (511kB)\n",
      "Requirement already up-to-date: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "Requirement already up-to-date: python-dateutil>=2.0 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->-r requirements.txt (line 2))\n",
      "Collecting pandas (from seaborn>=0.7.1->-r requirements.txt (line 6))\n",
      "  Downloading pandas-0.21.0-cp27-cp27mu-manylinux1_x86_64.whl (24.3MB)\n",
      "Collecting future>=0.15.2 (from autograd>=1.1.7->-r requirements.txt (line 8))\n",
      "  Downloading future-0.16.0.tar.gz (824kB)\n",
      "Building wheels for collected packages: autograd, future\n",
      "  Running setup.py bdist_wheel for autograd: started\n",
      "  Running setup.py bdist_wheel for autograd: finished with status 'done'\n",
      "  Stored in directory: /home/nbuser/.cache/pip/wheels/ea/d4/2d/e7d74fb5d727853026c54f23fa96f0b8defefc7d57dcaca0aa\n",
      "  Running setup.py bdist_wheel for future: started\n",
      "  Running setup.py bdist_wheel for future: finished with status 'done'\n",
      "  Stored in directory: /home/nbuser/.cache/pip/wheels/c2/50/7c/0d83b4baac4f63ff7a765bd16390d2ab43c93587fac9d6017a\n",
      "Successfully built autograd future\n",
      "Installing collected packages: backports.functools-lru-cache, pytz, matplotlib, scikit-learn, scipy, future, autograd, pandas\n",
      "  Found existing installation: backports.functools-lru-cache 1.2.1\n",
      "    Uninstalling backports.functools-lru-cache-1.2.1:\n",
      "      Successfully uninstalled backports.functools-lru-cache-1.2.1\n",
      "  Found existing installation: pytz 2017.2\n",
      "    Uninstalling pytz-2017.2:\n",
      "      Successfully uninstalled pytz-2017.2\n",
      "  Found existing installation: matplotlib 2.0.2\n",
      "    Uninstalling matplotlib-2.0.2:\n",
      "      Successfully uninstalled matplotlib-2.0.2\n",
      "  Found existing installation: scikit-learn 0.18.1\n",
      "    Uninstalling scikit-learn-0.18.1:\n",
      "      Successfully uninstalled scikit-learn-0.18.1\n",
      "  Found existing installation: scipy 0.19.0\n",
      "    Uninstalling scipy-0.19.0:\n",
      "      Successfully uninstalled scipy-0.19.0\n",
      "  Found existing installation: future 0.15.2\n",
      "    Uninstalling future-0.15.2:\n",
      "      Successfully uninstalled future-0.15.2\n",
      "  Found existing installation: pandas 0.19.2\n",
      "    Uninstalling pandas-0.19.2:\n",
      "      Successfully uninstalled pandas-0.19.2\n",
      "Successfully installed autograd-1.2 backports.functools-lru-cache-1.4 future-0.16.0 matplotlib-2.1.0 pandas-0.21.0 pytz-2017.3 scikit-learn-0.19.1 scipy-1.0.0\n",
      "Processing /home/nbuser/library/dl/MLAlgorithms\n",
      "Requirement already satisfied: tqdm in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: matplotlib>=1.5.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: numpy>=1.11.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: scikit-learn>=0.18 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: scipy>=0.18.0 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: seaborn>=0.7.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: six>=1.10.0 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: autograd>=1.1.7 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from mla==0.0.1)\n",
      "Requirement already satisfied: subprocess32 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: backports.functools-lru-cache in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: pytz in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.0 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->mla==0.0.1)\n",
      "Requirement already satisfied: pandas in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from seaborn>=0.7.1->mla==0.0.1)\n",
      "Requirement already satisfied: future>=0.15.2 in /home/nbcommon/anaconda2_501/lib/python2.7/site-packages (from autograd>=1.1.7->mla==0.0.1)\n",
      "Installing collected packages: mla\n",
      "  Running setup.py install for mla: started\n",
      "    Running setup.py install for mla: finished with status 'done'\n",
      "Successfully installed mla-0.0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory '/github/': Permission denied\n",
      "bash: line 2: cd: /github/: No such file or directory\n",
      "fatal: destination path 'MLAlgorithms' already exists and is not an empty directory.\n",
      "    DEPRECATION: Uninstalling a distutils installed project (scikit-learn) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.\n",
      "    DEPRECATION: Uninstalling a distutils installed project (future) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir /github/\n",
    "cd /github/\n",
    "git clone --depth=1 https://github.com/rushter/MLAlgorithms\n",
    "cd MLAlgorithms\n",
    "pip install -U -r requirements.txt\n",
    "pip install ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bash: line 1: cd: /space/dl/: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd /space/dl/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "try:\n",
    "    from sklearn.model_selection import train_test_split\n",
    "except ImportError:\n",
    "    from sklearn.cross_validation import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "from mla.linear_models import LinearRegression, LogisticRegression\n",
    "from mla.metrics.metrics import mean_squared_error, accuracy\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "logging.basicConfig(level=logging.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def regression():\n",
    "    # Generate a random regression problem\n",
    "    X, y = make_regression(n_samples=10000, n_features=100,\n",
    "                           n_informative=75, n_targets=1, noise=0.05,\n",
    "                           random_state=1111, bias=0.5)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\n",
    "                                                        random_state=1111)\n",
    "\n",
    "    model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    print('regression mse', mean_squared_error(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 1, error 291864.694578\n",
      "INFO:root:Iteration 2, error 279967.384394\n",
      "INFO:root:Iteration 3, error 268563.310601\n",
      "INFO:root:Iteration 4, error 257631.70041\n",
      "INFO:root:Iteration 5, error 247152.668588\n",
      "INFO:root:Iteration 6, error 237107.179039\n",
      "INFO:root:Iteration 7, error 227477.008064\n",
      "INFO:root:Iteration 8, error 218244.709234\n",
      "INFO:root:Iteration 9, error 209393.579797\n",
      "INFO:root:Iteration 10, error 200907.628556\n",
      "INFO:root:Iteration 11, error 192771.545151\n",
      "INFO:root:Iteration 12, error 184970.670677\n",
      "INFO:root:Iteration 13, error 177490.969598\n",
      "INFO:root:Iteration 14, error 170319.002871\n",
      "INFO:root:Iteration 15, error 163441.902258\n",
      "INFO:root:Iteration 16, error 156847.345743\n",
      "INFO:root:Iteration 17, error 150523.534034\n",
      "INFO:root:Iteration 18, error 144459.168081\n",
      "INFO:root:Iteration 19, error 138643.42757\n",
      "INFO:root:Iteration 20, error 133065.950366\n",
      "INFO:root:Iteration 21, error 127716.812831\n",
      "INFO:root:Iteration 22, error 122586.511016\n",
      "INFO:root:Iteration 23, error 117665.94266\n",
      "INFO:root:Iteration 24, error 112946.389969\n",
      "INFO:root:Iteration 25, error 108419.503153\n",
      "INFO:root:Iteration 26, error 104077.284667\n",
      "INFO:root:Iteration 27, error 99912.0741371\n",
      "INFO:root:Iteration 28, error 95916.5339475\n",
      "INFO:root:Iteration 29, error 92083.635441\n",
      "INFO:root:Iteration 30, error 88406.6457247\n",
      "INFO:root:Iteration 31, error 84879.1150432\n",
      "INFO:root:Iteration 32, error 81494.8646989\n",
      "INFO:root:Iteration 33, error 78247.9754946\n",
      "INFO:root:Iteration 34, error 75132.7766746\n",
      "INFO:root:Iteration 35, error 72143.8353439\n",
      "INFO:root:Iteration 36, error 69275.9463443\n",
      "INFO:root:Iteration 37, error 66524.1225665\n",
      "INFO:root:Iteration 38, error 63883.5856802\n",
      "INFO:root:Iteration 39, error 61349.7572637\n",
      "INFO:root:Iteration 40, error 58918.2503152\n",
      "INFO:root:Iteration 41, error 56584.8611297\n",
      "INFO:root:Iteration 42, error 54345.5615254\n",
      "INFO:root:Iteration 43, error 52196.4914037\n",
      "INFO:root:Iteration 44, error 50133.9516297\n",
      "INFO:root:Iteration 45, error 48154.3972179\n",
      "INFO:root:Iteration 46, error 46254.4308106\n",
      "INFO:root:Iteration 47, error 44430.796436\n",
      "INFO:root:Iteration 48, error 42680.3735336\n",
      "INFO:root:Iteration 49, error 41000.1712364\n",
      "INFO:root:Iteration 50, error 39387.3228972\n",
      "INFO:root:Iteration 51, error 37839.0808491\n",
      "INFO:root:Iteration 52, error 36352.8113912\n",
      "INFO:root:Iteration 53, error 34925.9899871\n",
      "INFO:root:Iteration 54, error 33556.1966699\n",
      "INFO:root:Iteration 55, error 32241.1116426\n",
      "INFO:root:Iteration 56, error 30978.5110667\n",
      "INFO:root:Iteration 57, error 29766.2630301\n",
      "INFO:root:Iteration 58, error 28602.3236872\n",
      "INFO:root:Iteration 59, error 27484.733563\n",
      "INFO:root:Iteration 60, error 26411.6140152\n",
      "INFO:root:Iteration 61, error 25381.1638462\n",
      "INFO:root:Iteration 62, error 24391.6560593\n",
      "INFO:root:Iteration 63, error 23441.4347536\n",
      "INFO:root:Iteration 64, error 22528.9121493\n",
      "INFO:root:Iteration 65, error 21652.5657406\n",
      "INFO:root:Iteration 66, error 20810.9355687\n",
      "INFO:root:Iteration 67, error 20002.6216107\n",
      "INFO:root:Iteration 68, error 19226.2812788\n",
      "INFO:root:Iteration 69, error 18480.6270261\n",
      "INFO:root:Iteration 70, error 17764.424053\n",
      "INFO:root:Iteration 71, error 17076.4881106\n",
      "INFO:root:Iteration 72, error 16415.6833981\n",
      "INFO:root:Iteration 73, error 15780.9205471\n",
      "INFO:root:Iteration 74, error 15171.1546931\n",
      "INFO:root:Iteration 75, error 14585.3836265\n",
      "INFO:root:Iteration 76, error 14022.6460231\n",
      "INFO:root:Iteration 77, error 13482.0197478\n",
      "INFO:root:Iteration 78, error 12962.6202312\n",
      "INFO:root:Iteration 79, error 12463.5989129\n",
      "INFO:root:Iteration 80, error 11984.141752\n",
      "INFO:root:Iteration 81, error 11523.4677984\n",
      "INFO:root:Iteration 82, error 11080.827826\n",
      "INFO:root:Iteration 83, error 10655.5030215\n",
      "INFO:root:Iteration 84, error 10246.8037297\n",
      "INFO:root:Iteration 85, error 9854.06825033\n",
      "INFO:root:Iteration 86, error 9476.66168595\n",
      "INFO:root:Iteration 87, error 9113.97483774\n",
      "INFO:root:Iteration 88, error 8765.42314772\n",
      "INFO:root:Iteration 89, error 8430.44568504\n",
      "INFO:root:Iteration 90, error 8108.50417479\n",
      "INFO:root:Iteration 91, error 7799.08206723\n",
      "INFO:root:Iteration 92, error 7501.68364598\n",
      "INFO:root:Iteration 93, error 7215.83317336\n",
      "INFO:root:Iteration 94, error 6941.07407139\n",
      "INFO:root:Iteration 95, error 6676.96813683\n",
      "INFO:root:Iteration 96, error 6423.09478903\n",
      "INFO:root:Iteration 97, error 6179.05034894\n",
      "INFO:root:Iteration 98, error 5944.4473482\n",
      "INFO:root:Iteration 99, error 5718.91386688\n",
      "INFO:root:Iteration 100, error 5502.09289874\n",
      "INFO:root:Iteration 101, error 5293.64174278\n",
      "INFO:root:Iteration 102, error 5093.23142015\n",
      "INFO:root:Iteration 103, error 4900.54611506\n",
      "INFO:root:Iteration 104, error 4715.28263893\n",
      "INFO:root:Iteration 105, error 4537.14991677\n",
      "INFO:root:Iteration 106, error 4365.86849465\n",
      "INFO:root:Iteration 107, error 4201.17006767\n",
      "INFO:root:Iteration 108, error 4042.79702736\n",
      "INFO:root:Iteration 109, error 3890.50202776\n",
      "INFO:root:Iteration 110, error 3744.04756941\n",
      "INFO:root:Iteration 111, error 3603.20560049\n",
      "INFO:root:Iteration 112, error 3467.75713437\n",
      "INFO:root:Iteration 113, error 3337.49188293\n",
      "INFO:root:Iteration 114, error 3212.20790496\n",
      "INFO:root:Iteration 115, error 3091.71126897\n",
      "INFO:root:Iteration 116, error 2975.81572989\n",
      "INFO:root:Iteration 117, error 2864.34241906\n",
      "INFO:root:Iteration 118, error 2757.11954688\n",
      "INFO:root:Iteration 119, error 2653.9821177\n",
      "INFO:root:Iteration 120, error 2554.77165636\n",
      "INFO:root:Iteration 121, error 2459.33594592\n",
      "INFO:root:Iteration 122, error 2367.52877619\n",
      "INFO:root:Iteration 123, error 2279.20970241\n",
      "INFO:root:Iteration 124, error 2194.24381392\n",
      "INFO:root:Iteration 125, error 2112.50151216\n",
      "INFO:root:Iteration 126, error 2033.85829785\n",
      "INFO:root:Iteration 127, error 1958.19456672\n",
      "INFO:root:Iteration 128, error 1885.39541366\n",
      "INFO:root:Iteration 129, error 1815.35044477\n",
      "INFO:root:Iteration 130, error 1747.95359712\n",
      "INFO:root:Iteration 131, error 1683.10296578\n",
      "INFO:root:Iteration 132, error 1620.70063792\n",
      "INFO:root:Iteration 133, error 1560.65253363\n",
      "INFO:root:Iteration 134, error 1502.86825319\n",
      "INFO:root:Iteration 135, error 1447.26093052\n",
      "INFO:root:Iteration 136, error 1393.74709263\n",
      "INFO:root:Iteration 137, error 1342.24652465\n",
      "INFO:root:Iteration 138, error 1292.68214046\n",
      "INFO:root:Iteration 139, error 1244.97985841\n",
      "INFO:root:Iteration 140, error 1199.06848215\n",
      "INFO:root:Iteration 141, error 1154.87958624\n",
      "INFO:root:Iteration 142, error 1112.3474064\n",
      "INFO:root:Iteration 143, error 1071.40873413\n",
      "INFO:root:Iteration 144, error 1032.00281562\n",
      "INFO:root:Iteration 145, error 994.071254742\n",
      "INFO:root:Iteration 146, error 957.557919881\n",
      "INFO:root:Iteration 147, error 922.408854576\n",
      "INFO:root:Iteration 148, error 888.572191714\n",
      "INFO:root:Iteration 149, error 855.998071183\n",
      "INFO:root:Iteration 150, error 824.638560817\n",
      "INFO:root:Iteration 151, error 794.447580521\n",
      "INFO:root:Iteration 152, error 765.380829428\n",
      "INFO:root:Iteration 153, error 737.395715971\n",
      "INFO:root:Iteration 154, error 710.451290761\n",
      "INFO:root:Iteration 155, error 684.508182131\n",
      "INFO:root:Iteration 156, error 659.528534272\n",
      "INFO:root:Iteration 157, error 635.475947826\n",
      "INFO:root:Iteration 158, error 612.315422855\n",
      "INFO:root:Iteration 159, error 590.013304076\n",
      "INFO:root:Iteration 160, error 568.537228286\n",
      "INFO:root:Iteration 161, error 547.85607387\n",
      "INFO:root:Iteration 162, error 527.939912325\n",
      "INFO:root:Iteration 163, error 508.759961703\n",
      "INFO:root:Iteration 164, error 490.288541909\n",
      "INFO:root:Iteration 165, error 472.499031769\n",
      "INFO:root:Iteration 166, error 455.365827802\n",
      "INFO:root:Iteration 167, error 438.864304626\n",
      "INFO:root:Iteration 168, error 422.97077693\n",
      "INFO:root:Iteration 169, error 407.662462951\n",
      "INFO:root:Iteration 170, error 392.917449397\n",
      "INFO:root:Iteration 171, error 378.714657755\n",
      "INFO:root:Iteration 172, error 365.033811928\n",
      "INFO:root:Iteration 173, error 351.855407151\n",
      "INFO:root:Iteration 174, error 339.160680129\n",
      "INFO:root:Iteration 175, error 326.931580358\n",
      "INFO:root:Iteration 176, error 315.150742565\n",
      "INFO:root:Iteration 177, error 303.801460243\n",
      "INFO:root:Iteration 178, error 292.867660221\n",
      "INFO:root:Iteration 179, error 282.33387823\n",
      "INFO:root:Iteration 180, error 272.185235437\n",
      "INFO:root:Iteration 181, error 262.407415889\n",
      "INFO:root:Iteration 182, error 252.98664485\n",
      "INFO:root:Iteration 183, error 243.909667982\n",
      "INFO:root:Iteration 184, error 235.163731343\n",
      "INFO:root:Iteration 185, error 226.736562163\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 186, error 218.61635038\n",
      "INFO:root:Iteration 187, error 210.791730886\n",
      "INFO:root:Iteration 188, error 203.251766478\n",
      "INFO:root:Iteration 189, error 195.985931464\n",
      "INFO:root:Iteration 190, error 188.984095914\n",
      "INFO:root:Iteration 191, error 182.236510521\n",
      "INFO:root:Iteration 192, error 175.733792054\n",
      "INFO:root:Iteration 193, error 169.466909372\n",
      "INFO:root:Iteration 194, error 163.427169992\n",
      "INFO:root:Iteration 195, error 157.606207164\n",
      "INFO:root:Iteration 196, error 151.995967459\n",
      "INFO:root:Iteration 197, error 146.588698834\n",
      "INFO:root:Iteration 198, error 141.376939163\n",
      "INFO:root:Iteration 199, error 136.353505202\n",
      "INFO:root:Iteration 200, error 131.511481998\n",
      "INFO:root:Iteration 201, error 126.844212689\n",
      "INFO:root:Iteration 202, error 122.345288715\n",
      "INFO:root:Iteration 203, error 118.008540393\n",
      "INFO:root:Iteration 204, error 113.828027865\n",
      "INFO:root:Iteration 205, error 109.798032392\n",
      "INFO:root:Iteration 206, error 105.913047984\n",
      "INFO:root:Iteration 207, error 102.167773349\n",
      "INFO:root:Iteration 208, error 98.5571041592\n",
      "INFO:root:Iteration 209, error 95.0761256058\n",
      "INFO:root:Iteration 210, error 91.7201052468\n",
      "INFO:root:Iteration 211, error 88.4844861264\n",
      "INFO:root:Iteration 212, error 85.3648801586\n",
      "INFO:root:Iteration 213, error 82.357061765\n",
      "INFO:root:Iteration 214, error 79.4569617565\n",
      "INFO:root:Iteration 215, error 76.660661449\n",
      "INFO:root:Iteration 216, error 73.9643870043\n",
      "INFO:root:Iteration 217, error 71.3645039875\n",
      "INFO:root:Iteration 218, error 68.8575121326\n",
      "INFO:root:Iteration 219, error 66.4400403076\n",
      "INFO:root:Iteration 220, error 64.1088416721\n",
      "INFO:root:Iteration 221, error 61.8607890192\n",
      "INFO:root:Iteration 222, error 59.6928702955\n",
      "INFO:root:Iteration 223, error 57.602184291\n",
      "INFO:root:Iteration 224, error 55.5859364937\n",
      "INFO:root:Iteration 225, error 53.6414351014\n",
      "INFO:root:Iteration 226, error 51.7660871854\n",
      "INFO:root:Iteration 227, error 49.9573949994\n",
      "INFO:root:Iteration 228, error 48.2129524293\n",
      "INFO:root:Iteration 229, error 46.5304415769\n",
      "INFO:root:Iteration 230, error 44.9076294737\n",
      "INFO:root:Iteration 231, error 43.3423649183\n",
      "INFO:root:Iteration 232, error 41.8325754347\n",
      "INFO:root:Iteration 233, error 40.3762643447\n",
      "INFO:root:Iteration 234, error 38.9715079514\n",
      "INFO:root:Iteration 235, error 37.6164528288\n",
      "INFO:root:Iteration 236, error 36.3093132144\n",
      "INFO:root:Iteration 237, error 35.048368499\n",
      "INFO:root:Iteration 238, error 33.8319608124\n",
      "INFO:root:Iteration 239, error 32.6584926994\n",
      "INFO:root:Iteration 240, error 31.5264248828\n",
      "INFO:root:Iteration 241, error 30.434274112\n",
      "INFO:root:Iteration 242, error 29.380611091\n",
      "INFO:root:Iteration 243, error 28.3640584851\n",
      "INFO:root:Iteration 244, error 27.3832890024\n",
      "INFO:root:Iteration 245, error 26.4370235466\n",
      "INFO:root:Iteration 246, error 25.5240294405\n",
      "INFO:root:Iteration 247, error 24.6431187141\n",
      "INFO:root:Iteration 248, error 23.7931464589\n",
      "INFO:root:Iteration 249, error 22.9730092422\n",
      "INFO:root:Iteration 250, error 22.1816435812\n",
      "INFO:root:Iteration 251, error 21.4180244749\n",
      "INFO:root:Iteration 252, error 20.6811639894\n",
      "INFO:root:Iteration 253, error 19.9701098974\n",
      "INFO:root:Iteration 254, error 19.2839443675\n",
      "INFO:root:Iteration 255, error 18.6217827029\n",
      "INFO:root:Iteration 256, error 17.9827721266\n",
      "INFO:root:Iteration 257, error 17.3660906127\n",
      "INFO:root:Iteration 258, error 16.7709457599\n",
      "INFO:root:Iteration 259, error 16.1965737085\n",
      "INFO:root:Iteration 260, error 15.6422380958\n",
      "INFO:root:Iteration 261, error 15.1072290521\n",
      "INFO:root:Iteration 262, error 14.5908622328\n",
      "INFO:root:Iteration 263, error 14.0924778864\n",
      "INFO:root:Iteration 264, error 13.6114399582\n",
      "INFO:root:Iteration 265, error 13.1471352255\n",
      "INFO:root:Iteration 266, error 12.6989724663\n",
      "INFO:root:Iteration 267, error 12.2663816581\n",
      "INFO:root:Iteration 268, error 11.8488132061\n",
      "INFO:root:Iteration 269, error 11.4457372006\n",
      "INFO:root:Iteration 270, error 11.056642701\n",
      "INFO:root:Iteration 271, error 10.6810370472\n",
      "INFO:root:Iteration 272, error 10.3184451955\n",
      "INFO:root:Iteration 273, error 9.96840907941\n",
      "INFO:root:Iteration 274, error 9.63048699407\n",
      "INFO:root:Iteration 275, error 9.30425300313\n",
      "INFO:root:Iteration 276, error 8.98929636756\n",
      "INFO:root:Iteration 277, error 8.68522099544\n",
      "INFO:root:Iteration 278, error 8.39164491201\n",
      "INFO:root:Iteration 279, error 8.10819974913\n",
      "INFO:root:Iteration 280, error 7.83453025352\n",
      "INFO:root:Iteration 281, error 7.57029381301\n",
      "INFO:root:Iteration 282, error 7.31516000018\n",
      "INFO:root:Iteration 283, error 7.06881013275\n",
      "INFO:root:Iteration 284, error 6.83093684998\n",
      "INFO:root:Iteration 285, error 6.60124370469\n",
      "INFO:root:Iteration 286, error 6.37944477011\n",
      "INFO:root:Iteration 287, error 6.16526426112\n",
      "INFO:root:Iteration 288, error 5.95843616939\n",
      "INFO:root:Iteration 289, error 5.75870391175\n",
      "INFO:root:Iteration 290, error 5.56581999147\n",
      "INFO:root:Iteration 291, error 5.37954567183\n",
      "INFO:root:Iteration 292, error 5.19965066164\n",
      "INFO:root:Iteration 293, error 5.02591281217\n",
      "INFO:root:Iteration 294, error 4.85811782518\n",
      "INFO:root:Iteration 295, error 4.69605897147\n",
      "INFO:root:Iteration 296, error 4.53953681977\n",
      "INFO:root:Iteration 297, error 4.38835897544\n",
      "INFO:root:Iteration 298, error 4.24233982862\n",
      "INFO:root:Iteration 299, error 4.10130031162\n",
      "INFO:root:Iteration 300, error 3.965067665\n",
      "INFO:root:Iteration 301, error 3.83347521222\n",
      "INFO:root:Iteration 302, error 3.70636214242\n",
      "INFO:root:Iteration 303, error 3.58357330107\n",
      "INFO:root:Iteration 304, error 3.4649589882\n",
      "INFO:root:Iteration 305, error 3.35037476394\n",
      "INFO:root:Iteration 306, error 3.23968126106\n",
      "INFO:root:Iteration 307, error 3.13274400434\n",
      "INFO:root:Iteration 308, error 3.0294332364\n",
      "INFO:root:Iteration 309, error 2.92962374987\n",
      "INFO:root:Iteration 310, error 2.83319472561\n",
      "INFO:root:Iteration 311, error 2.7400295767\n",
      "INFO:root:Iteration 312, error 2.65001579815\n",
      "INFO:root:Iteration 313, error 2.56304482196\n",
      "INFO:root:Iteration 314, error 2.4790118774\n",
      "INFO:root:Iteration 315, error 2.39781585634\n",
      "INFO:root:Iteration 316, error 2.31935918344\n",
      "INFO:root:Iteration 317, error 2.24354769092\n",
      "INFO:root:Iteration 318, error 2.17029049794\n",
      "INFO:root:Iteration 319, error 2.09949989426\n",
      "INFO:root:Iteration 320, error 2.03109122803\n",
      "INFO:root:Iteration 321, error 1.9649827977\n",
      "INFO:root:Iteration 322, error 1.90109574771\n",
      "INFO:root:Iteration 323, error 1.839353968\n",
      "INFO:root:Iteration 324, error 1.77968399705\n",
      "INFO:root:Iteration 325, error 1.72201492843\n",
      "INFO:root:Iteration 326, error 1.66627832069\n",
      "INFO:root:Iteration 327, error 1.61240811046\n",
      "INFO:root:Iteration 328, error 1.56034052864\n",
      "INFO:root:Iteration 329, error 1.51001401964\n",
      "INFO:root:Iteration 330, error 1.46136916343\n",
      "INFO:root:Iteration 331, error 1.41434860044\n",
      "INFO:root:Iteration 332, error 1.36889695908\n",
      "INFO:root:Iteration 333, error 1.32496078586\n",
      "INFO:root:Iteration 334, error 1.28248847806\n",
      "INFO:root:Iteration 335, error 1.24143021867\n",
      "INFO:root:Iteration 336, error 1.20173791381\n",
      "INFO:root:Iteration 337, error 1.16336513219\n",
      "INFO:root:Iteration 338, error 1.12626704689\n",
      "INFO:root:Iteration 339, error 1.09040037912\n",
      "INFO:root:Iteration 340, error 1.05572334397\n",
      "INFO:root:Iteration 341, error 1.02219559815\n",
      "INFO:root:Iteration 342, error 0.989778189508\n",
      "INFO:root:Iteration 343, error 0.958433508403\n",
      "INFO:root:Iteration 344, error 0.928125240758\n",
      "INFO:root:Iteration 345, error 0.898818322802\n",
      "INFO:root:Iteration 346, error 0.870478897396\n",
      "INFO:root:Iteration 347, error 0.843074271918\n",
      "INFO:root:Iteration 348, error 0.816572877631\n",
      "INFO:root:Iteration 349, error 0.790944230493\n",
      "INFO:root:Iteration 350, error 0.766158893345\n",
      "INFO:root:Iteration 351, error 0.742188439437\n",
      "INFO:root:Iteration 352, error 0.719005417251\n",
      "INFO:root:Iteration 353, error 0.696583316546\n",
      "INFO:root:Iteration 354, error 0.674896535622\n",
      "INFO:root:Iteration 355, error 0.65392034972\n",
      "INFO:root:Iteration 356, error 0.633630880551\n",
      "INFO:root:Iteration 357, error 0.614005066883\n",
      "INFO:root:Iteration 358, error 0.595020636177\n",
      "INFO:root:Iteration 359, error 0.576656077211\n",
      "INFO:root:Iteration 360, error 0.558890613674\n",
      "INFO:root:Iteration 361, error 0.541704178683\n",
      "INFO:root:Iteration 362, error 0.525077390197\n",
      "INFO:root:Iteration 363, error 0.508991527298\n",
      "INFO:root:Iteration 364, error 0.493428507297\n",
      "INFO:root:Iteration 365, error 0.47837086365\n",
      "INFO:root:Iteration 366, error 0.463801724647\n",
      "INFO:root:Iteration 367, error 0.449704792845\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 368, error 0.436064325225\n",
      "INFO:root:Iteration 369, error 0.422865114041\n",
      "INFO:root:Iteration 370, error 0.410092468344\n",
      "INFO:root:Iteration 371, error 0.397732196145\n",
      "INFO:root:Iteration 372, error 0.385770587209\n",
      "INFO:root:Iteration 373, error 0.374194396448\n",
      "INFO:root:Iteration 374, error 0.36299082789\n",
      "INFO:root:Iteration 375, error 0.352147519215\n",
      "INFO:root:Iteration 376, error 0.341652526826\n",
      "INFO:root:Iteration 377, error 0.331494311441\n",
      "INFO:root:Iteration 378, error 0.321661724191\n",
      "INFO:root:Iteration 379, error 0.312143993196\n",
      "INFO:root:Iteration 380, error 0.302930710617\n",
      "INFO:root:Iteration 381, error 0.294011820153\n",
      "INFO:root:Iteration 382, error 0.285377604974\n",
      "INFO:root:Iteration 383, error 0.277018676076\n",
      "INFO:root:Iteration 384, error 0.268925961039\n",
      "INFO:root:Iteration 385, error 0.261090693175\n",
      "INFO:root:Iteration 386, error 0.253504401058\n",
      "INFO:root:Iteration 387, error 0.24615889841\n",
      "INFO:root:Iteration 388, error 0.239046274346\n",
      "INFO:root:Iteration 389, error 0.232158883949\n",
      "INFO:root:Iteration 390, error 0.225489339183\n",
      "INFO:root:Iteration 391, error 0.219030500108\n",
      "INFO:root:Iteration 392, error 0.212775466407\n",
      "INFO:root:Iteration 393, error 0.20671756921\n",
      "INFO:root:Iteration 394, error 0.200850363192\n",
      "INFO:root:Iteration 395, error 0.195167618947\n",
      "INFO:root:Iteration 396, error 0.189663315634\n",
      "INFO:root:Iteration 397, error 0.184331633864\n",
      "INFO:root:Iteration 398, error 0.179166948845\n",
      "INFO:root:Iteration 399, error 0.174163823753\n",
      "INFO:root:Iteration 400, error 0.169317003342\n",
      "INFO:root:Iteration 401, error 0.164621407765\n",
      "INFO:root:Iteration 402, error 0.160072126617\n",
      "INFO:root:Iteration 403, error 0.155664413176\n",
      "INFO:root:Iteration 404, error 0.151393678847\n",
      "INFO:root:Iteration 405, error 0.147255487795\n",
      "INFO:root:Iteration 406, error 0.143245551766\n",
      "INFO:root:Iteration 407, error 0.139359725079\n",
      "INFO:root:Iteration 408, error 0.135593999803\n",
      "INFO:root:Iteration 409, error 0.131944501082\n",
      "INFO:root:Iteration 410, error 0.12840748264\n",
      "INFO:root:Iteration 411, error 0.124979322425\n",
      "INFO:root:Iteration 412, error 0.121656518412\n",
      "INFO:root:Iteration 413, error 0.118435684544\n",
      "INFO:root:Iteration 414, error 0.115313546817\n",
      "INFO:root:Iteration 415, error 0.112286939497\n",
      "INFO:root:Iteration 416, error 0.109352801465\n",
      "INFO:root:Iteration 417, error 0.106508172691\n",
      "INFO:root:Iteration 418, error 0.103750190825\n",
      "INFO:root:Iteration 419, error 0.101076087908\n",
      "INFO:root:Iteration 420, error 0.0984831871929\n",
      "INFO:root:Iteration 421, error 0.0959689000759\n",
      "INFO:root:Iteration 422, error 0.093530723131\n",
      "INFO:root:Iteration 423, error 0.0911662352474\n",
      "INFO:root:Iteration 424, error 0.088873094864\n",
      "INFO:root:Iteration 425, error 0.0866490372984\n",
      "INFO:root:Iteration 426, error 0.0844918721665\n",
      "INFO:root:Iteration 427, error 0.0823994808913\n",
      "INFO:root:Iteration 428, error 0.0803698142951\n",
      "INFO:root:Iteration 429, error 0.0784008902751\n",
      "INFO:root:Iteration 430, error 0.0764907915569\n",
      "INFO:root:Iteration 431, error 0.0746376635256\n",
      "INFO:root:Iteration 432, error 0.0728397121299\n",
      "INFO:root:Iteration 433, error 0.071095201858\n",
      "INFO:root:Iteration 434, error 0.0694024537814\n",
      "INFO:root:Iteration 435, error 0.0677598436667\n",
      "INFO:root:Iteration 436, error 0.0661658001497\n",
      "INFO:root:Iteration 437, error 0.0646188029731\n",
      "INFO:root:Iteration 438, error 0.0631173812825\n",
      "INFO:root:Iteration 439, error 0.0616601119817\n",
      "INFO:root:Iteration 440, error 0.0602456181424\n",
      "INFO:root:Iteration 441, error 0.0588725674684\n",
      "INFO:root:Iteration 442, error 0.057539670812\n",
      "INFO:root:Iteration 443, error 0.0562456807401\n",
      "INFO:root:Iteration 444, error 0.0549893901491\n",
      "INFO:root:Iteration 445, error 0.0537696309264\n",
      "INFO:root:Iteration 446, error 0.0525852726576\n",
      "INFO:root:Iteration 447, error 0.0514352213769\n",
      "INFO:root:Iteration 448, error 0.0503184183595\n",
      "INFO:root:Iteration 449, error 0.0492338389559\n",
      "INFO:root:Iteration 450, error 0.0481804914638\n",
      "INFO:root:Iteration 451, error 0.0471574160394\n",
      "INFO:root:Iteration 452, error 0.0461636836449\n",
      "INFO:root:Iteration 453, error 0.0451983950313\n",
      "INFO:root:Iteration 454, error 0.0442606797556\n",
      "INFO:root:Iteration 455, error 0.0433496952312\n",
      "INFO:root:Iteration 456, error 0.0424646258097\n",
      "INFO:root:Iteration 457, error 0.0416046818944\n",
      "INFO:root:Iteration 458, error 0.0407690990827\n",
      "INFO:root:Iteration 459, error 0.0399571373379\n",
      "INFO:root:Iteration 460, error 0.0391680801887\n",
      "INFO:root:Iteration 461, error 0.0384012339549\n",
      "INFO:root:Iteration 462, error 0.0376559270002\n",
      "INFO:root:Iteration 463, error 0.0369315090091\n",
      "INFO:root:Iteration 464, error 0.0362273502883\n",
      "INFO:root:Iteration 465, error 0.0355428410915\n",
      "INFO:root:Iteration 466, error 0.034877390967\n",
      "INFO:root:Iteration 467, error 0.0342304281267\n",
      "INFO:root:Iteration 468, error 0.0336013988364\n",
      "INFO:root:Iteration 469, error 0.0329897668262\n",
      "INFO:root:Iteration 470, error 0.0323950127215\n",
      "INFO:root:Iteration 471, error 0.0318166334915\n",
      "INFO:root:Iteration 472, error 0.0312541419174\n",
      "INFO:root:Iteration 473, error 0.0307070660775\n",
      "INFO:root:Iteration 474, error 0.03017494885\n",
      "INFO:root:Iteration 475, error 0.0296573474318\n",
      "INFO:root:Iteration 476, error 0.0291538328739\n",
      "INFO:root:Iteration 477, error 0.0286639896316\n",
      "INFO:root:Iteration 478, error 0.0281874151304\n",
      "INFO:root:Iteration 479, error 0.0277237193455\n",
      "INFO:root:Iteration 480, error 0.027272524396\n",
      "INFO:root:Iteration 481, error 0.0268334641518\n",
      "INFO:root:Iteration 482, error 0.0264061838545\n",
      "INFO:root:Iteration 483, error 0.0259903397496\n",
      "INFO:root:Iteration 484, error 0.0255855987323\n",
      "INFO:root:Iteration 485, error 0.0251916380039\n",
      "INFO:root:Iteration 486, error 0.02480814474\n",
      "INFO:root:Iteration 487, error 0.0244348157695\n",
      "INFO:root:Iteration 488, error 0.0240713572647\n",
      "INFO:root:Iteration 489, error 0.023717484441\n",
      "INFO:root:Iteration 490, error 0.0233729212666\n",
      "INFO:root:Iteration 491, error 0.0230374001825\n",
      "INFO:root:Iteration 492, error 0.0227106618304\n",
      "INFO:root:Iteration 493, error 0.0223924547909\n",
      "INFO:root:Iteration 494, error 0.0220825353295\n",
      "INFO:root:Iteration 495, error 0.0217806671513\n",
      "INFO:root:Iteration 496, error 0.0214866211632\n",
      "INFO:root:Iteration 497, error 0.0212001752452\n",
      "INFO:root:Iteration 498, error 0.0209211140273\n",
      "INFO:root:Iteration 499, error 0.0206492286758\n",
      "INFO:root:Iteration 500, error 0.0203843166848\n",
      "INFO:root:Iteration 501, error 0.0201261816756\n",
      "INFO:root:Iteration 502, error 0.0198746332024\n",
      "INFO:root:Iteration 503, error 0.0196294865643\n",
      "INFO:root:Iteration 504, error 0.0193905626234\n",
      "INFO:root:Iteration 505, error 0.019157687629\n",
      "INFO:root:Iteration 506, error 0.0189306930472\n",
      "INFO:root:Iteration 507, error 0.0187094153967\n",
      "INFO:root:Iteration 508, error 0.018493696089\n",
      "INFO:root:Iteration 509, error 0.0182833812751\n",
      "INFO:root:Iteration 510, error 0.0180783216958\n",
      "INFO:root:Iteration 511, error 0.0178783725375\n",
      "INFO:root:Iteration 512, error 0.0176833932934\n",
      "INFO:root:Iteration 513, error 0.0174932476277\n",
      "INFO:root:Iteration 514, error 0.0173078032452\n",
      "INFO:root:Iteration 515, error 0.0171269317655\n",
      "INFO:root:Iteration 516, error 0.0169505085999\n",
      "INFO:root:Iteration 517, error 0.0167784128336\n",
      "INFO:root:Iteration 518, error 0.0166105271111\n",
      "INFO:root:Iteration 519, error 0.0164467375252\n",
      "INFO:root:Iteration 520, error 0.0162869335101\n",
      "INFO:root:Iteration 521, error 0.0161310077375\n",
      "INFO:root:Iteration 522, error 0.0159788560161\n",
      "INFO:root:Iteration 523, error 0.0158303771947\n",
      "INFO:root:Iteration 524, error 0.0156854730677\n",
      "INFO:root:Iteration 525, error 0.0155440482845\n",
      "INFO:root:Iteration 526, error 0.0154060102612\n",
      "INFO:root:Iteration 527, error 0.0152712690954\n",
      "INFO:root:Iteration 528, error 0.0151397374837\n",
      "INFO:root:Iteration 529, error 0.0150113306415\n",
      "INFO:root:Iteration 530, error 0.0148859662261\n",
      "INFO:root:Iteration 531, error 0.0147635642615\n",
      "INFO:root:Iteration 532, error 0.0146440470663\n",
      "INFO:root:Iteration 533, error 0.0145273391832\n",
      "INFO:root:Iteration 534, error 0.0144133673111\n",
      "INFO:root:Iteration 535, error 0.0143020602396\n",
      "INFO:root:Iteration 536, error 0.0141933487852\n",
      "INFO:root:Iteration 537, error 0.0140871657296\n",
      "INFO:root:Iteration 538, error 0.0139834457601\n",
      "INFO:root:Iteration 539, error 0.0138821254119\n",
      "INFO:root:Iteration 540, error 0.013783143012\n",
      "INFO:root:Convergence has reached.\n",
      "INFO:root: Theta: [  4.94922071e-01   2.14346969e-03   5.31508178e+01   3.66771272e+01\n",
      "   3.12461022e-03   7.57801573e-04   3.58278589e+01   7.95279113e+01\n",
      "   8.15168075e+01   2.03225663e+01   2.55731805e-03   1.16738111e-03\n",
      "   2.62955206e+01   8.84066857e+01  -8.01413390e-03  -3.56965648e-03\n",
      "   8.91129128e+01   7.86043735e+01   6.96327071e-04   5.98050806e-04\n",
      "   9.26157265e+01   9.05855280e+01  -5.56224628e-03   4.55011115e+01\n",
      "   9.38630195e+01   6.74888522e+01   6.51573402e-03   6.86813275e+01\n",
      "   4.04983264e+00   4.72027158e+01   4.61015001e+01  -2.98167290e-03\n",
      "   7.90746829e+01  -1.66327293e-03   6.28777125e+01   3.38121680e+01\n",
      "   4.42202730e+01   5.13617027e+01   3.10409144e+01   7.13438631e+01\n",
      "  -1.68145560e-03   2.10592750e+01   5.00733013e-03   5.34594834e+01\n",
      "   3.73908590e+01   9.14657524e+01   3.41603853e+01   5.39635771e-03\n",
      "   8.46111773e+01   7.69936639e+01   7.05131681e+01   2.85871128e+01\n",
      "   3.90311487e+00   7.43855765e+01   5.90477867e+01   7.79830513e+01\n",
      "   9.25765818e+00   6.32280230e+01   7.54680253e+01   3.15762631e+00\n",
      "   1.11061336e+01   7.04273563e-04   8.32692286e+01   6.70682180e-03\n",
      "   4.96692553e+01   2.34871459e-04   5.83943728e+00   5.28387578e+01\n",
      "   5.19788886e+01   4.07084353e+01  -2.37786720e-04   9.87434651e+01\n",
      "  -5.54278751e-03   8.94609147e+01   9.31685555e+00   7.32142614e+01\n",
      "   1.04732080e+01   5.46508879e+01   3.92449926e+01   6.07645602e+01\n",
      "   9.14509925e+01   4.97697935e+01   7.71759637e-01   8.62605444e+01\n",
      "  -1.45867627e-04   8.60498663e+01   9.27388109e+01  -5.26542950e-04\n",
      "   1.35249194e-03   7.18842341e+01   5.20803642e+01   9.45029341e+01\n",
      "  -1.11203588e-03   9.85335533e+01   8.35275780e+01   2.83145780e+01\n",
      "   7.33046335e+01   3.45493471e+01   5.85581028e+01   5.57141087e+01\n",
      "   9.11651922e+01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('regression mse', 0.014299757949575767)\n"
     ]
    }
   ],
   "source": [
    "regression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = make_regression(n_samples=100, n_features=2,\n",
    "                           n_informative=75, n_targets=1, noise=0.05,\n",
    "                           random_state=1928, bias=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 100), (100,)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans = np.transpose(X)\n",
    "[X_trans.shape, y.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 1, error 4632.36675231\n",
      "INFO:root:Iteration 2, error 4404.296067\n",
      "INFO:root:Iteration 3, error 4187.54428294\n",
      "INFO:root:Iteration 4, error 3981.54619711\n",
      "INFO:root:Iteration 5, error 3785.76496216\n",
      "INFO:root:Iteration 6, error 3599.69065873\n",
      "INFO:root:Iteration 7, error 3422.83893997\n",
      "INFO:root:Iteration 8, error 3254.7497443\n",
      "INFO:root:Iteration 9, error 3094.98607328\n",
      "INFO:root:Iteration 10, error 2943.13283111\n",
      "INFO:root:Iteration 11, error 2798.79572266\n",
      "INFO:root:Iteration 12, error 2661.60020715\n",
      "INFO:root:Iteration 13, error 2531.1905046\n",
      "INFO:root:Iteration 14, error 2407.22865237\n",
      "INFO:root:Iteration 15, error 2289.39360934\n",
      "INFO:root:Iteration 16, error 2177.38040518\n",
      "INFO:root:Iteration 17, error 2070.89933266\n",
      "INFO:root:Iteration 18, error 1969.67518053\n",
      "INFO:root:Iteration 19, error 1873.44650515\n",
      "INFO:root:Iteration 20, error 1781.96493886\n",
      "INFO:root:Iteration 21, error 1694.99453313\n",
      "INFO:root:Iteration 22, error 1612.31113491\n",
      "INFO:root:Iteration 23, error 1533.70179428\n",
      "INFO:root:Iteration 24, error 1458.96420208\n",
      "INFO:root:Iteration 25, error 1387.90615575\n",
      "INFO:root:Iteration 26, error 1320.34505216\n",
      "INFO:root:Iteration 27, error 1256.10740593\n",
      "INFO:root:Iteration 28, error 1195.02839202\n",
      "INFO:root:Iteration 29, error 1136.95141135\n",
      "INFO:root:Iteration 30, error 1081.7276783\n",
      "INFO:root:Iteration 31, error 1029.21582893\n",
      "INFO:root:Iteration 32, error 979.281548965\n",
      "INFO:root:Iteration 33, error 931.797220475\n",
      "INFO:root:Iteration 34, error 886.641586321\n",
      "INFO:root:Iteration 35, error 843.699431499\n",
      "INFO:root:Iteration 36, error 802.8612805\n",
      "INFO:root:Iteration 37, error 764.023109892\n",
      "INFO:root:Iteration 38, error 727.086075348\n",
      "INFO:root:Iteration 39, error 691.956252405\n",
      "INFO:root:Iteration 40, error 658.544390243\n",
      "INFO:root:Iteration 41, error 626.765677843\n",
      "INFO:root:Iteration 42, error 596.539521896\n",
      "INFO:root:Iteration 43, error 567.789335865\n",
      "INFO:root:Iteration 44, error 540.442339641\n",
      "INFO:root:Iteration 45, error 514.429369267\n",
      "INFO:root:Iteration 46, error 489.684696209\n",
      "INFO:root:Iteration 47, error 466.145855702\n",
      "INFO:root:Iteration 48, error 443.753483713\n",
      "INFO:root:Iteration 49, error 422.451162085\n",
      "INFO:root:Iteration 50, error 402.185271451\n",
      "INFO:root:Iteration 51, error 382.904851524\n",
      "INFO:root:Iteration 52, error 364.5614684\n",
      "INFO:root:Iteration 53, error 347.109088508\n",
      "INFO:root:Iteration 54, error 330.503958884\n",
      "INFO:root:Iteration 55, error 314.704493448\n",
      "INFO:root:Iteration 56, error 299.671164976\n",
      "INFO:root:Iteration 57, error 285.366402486\n",
      "INFO:root:Iteration 58, error 271.754493766\n",
      "INFO:root:Iteration 59, error 258.801492787\n",
      "INFO:root:Iteration 60, error 246.475131742\n",
      "INFO:root:Iteration 61, error 234.7447375\n",
      "INFO:root:Iteration 62, error 223.581152237\n",
      "INFO:root:Iteration 63, error 212.956658036\n",
      "INFO:root:Iteration 64, error 202.844905265\n",
      "INFO:root:Iteration 65, error 193.220844536\n",
      "INFO:root:Iteration 66, error 184.060662063\n",
      "INFO:root:Iteration 67, error 175.341718255\n",
      "INFO:root:Iteration 68, error 167.04248938\n",
      "INFO:root:Iteration 69, error 159.142512143\n",
      "INFO:root:Iteration 70, error 151.622331028\n",
      "INFO:root:Iteration 71, error 144.463448283\n",
      "INFO:root:Iteration 72, error 137.648276386\n",
      "INFO:root:Iteration 73, error 131.160092899\n",
      "INFO:root:Iteration 74, error 124.982997568\n",
      "INFO:root:Iteration 75, error 119.101871563\n",
      "INFO:root:Iteration 76, error 113.502338757\n",
      "INFO:root:Iteration 77, error 108.17072893\n",
      "INFO:root:Iteration 78, error 103.094042812\n",
      "INFO:root:Iteration 79, error 98.2599188693\n",
      "INFO:root:Iteration 80, error 93.6566017396\n",
      "INFO:root:Iteration 81, error 89.2729122485\n",
      "INFO:root:Iteration 82, error 85.0982189133\n",
      "INFO:root:Iteration 83, error 81.1224108682\n",
      "INFO:root:Iteration 84, error 77.335872137\n",
      "INFO:root:Iteration 85, error 73.729457186\n",
      "INFO:root:Iteration 86, error 70.2944676937\n",
      "INFO:root:Iteration 87, error 67.0226304753\n",
      "INFO:root:Iteration 88, error 63.9060765055\n",
      "INFO:root:Iteration 89, error 60.9373209837\n",
      "INFO:root:Iteration 90, error 58.1092443902\n",
      "INFO:root:Iteration 91, error 55.4150744831\n",
      "INFO:root:Iteration 92, error 52.8483691899\n",
      "INFO:root:Iteration 93, error 50.4030003479\n",
      "INFO:root:Iteration 94, error 48.073138252\n",
      "INFO:root:Iteration 95, error 45.8532369691\n",
      "INFO:root:Iteration 96, error 43.7380203812\n",
      "INFO:root:Iteration 97, error 41.7224689201\n",
      "INFO:root:Iteration 98, error 39.8018069598\n",
      "INFO:root:Iteration 99, error 37.971490834\n",
      "INFO:root:Iteration 100, error 36.2271974467\n",
      "INFO:root:Iteration 101, error 34.5648134472\n",
      "INFO:root:Iteration 102, error 32.9804249404\n",
      "INFO:root:Iteration 103, error 31.4703077072\n",
      "INFO:root:Iteration 104, error 30.0309179073\n",
      "INFO:root:Iteration 105, error 28.6588832436\n",
      "INFO:root:Iteration 106, error 27.3509945613\n",
      "INFO:root:Iteration 107, error 26.1041978639\n",
      "INFO:root:Iteration 108, error 24.9155867223\n",
      "INFO:root:Iteration 109, error 23.7823950597\n",
      "INFO:root:Iteration 110, error 22.7019902918\n",
      "INFO:root:Iteration 111, error 21.6718668058\n",
      "INFO:root:Iteration 112, error 20.6896397615\n",
      "INFO:root:Iteration 113, error 19.7530391969\n",
      "INFO:root:Iteration 114, error 18.8599044254\n",
      "INFO:root:Iteration 115, error 18.0081787086\n",
      "INFO:root:Iteration 116, error 17.1959041907\n",
      "INFO:root:Iteration 117, error 16.421217084\n",
      "INFO:root:Iteration 118, error 15.6823430889\n",
      "INFO:root:Iteration 119, error 14.9775930417\n",
      "INFO:root:Iteration 120, error 14.3053587741\n",
      "INFO:root:Iteration 121, error 13.6641091776\n",
      "INFO:root:Iteration 122, error 13.0523864609\n",
      "INFO:root:Iteration 123, error 12.4688025905\n",
      "INFO:root:Iteration 124, error 11.9120359067\n",
      "INFO:root:Iteration 125, error 11.3808279051\n",
      "INFO:root:Iteration 126, error 10.8739801763\n",
      "INFO:root:Iteration 127, error 10.390351495\n",
      "INFO:root:Iteration 128, error 9.92885505261\n",
      "INFO:root:Iteration 129, error 9.48845582427\n",
      "INFO:root:Iteration 130, error 9.06816806561\n",
      "INFO:root:Iteration 131, error 8.66705293151\n",
      "INFO:root:Iteration 132, error 8.28421621139\n",
      "INFO:root:Iteration 133, error 7.91880617525\n",
      "INFO:root:Iteration 134, error 7.57001152479\n",
      "INFO:root:Iteration 135, error 7.23705944468\n",
      "INFO:root:Iteration 136, error 6.91921374884\n",
      "INFO:root:Iteration 137, error 6.61577311717\n",
      "INFO:root:Iteration 138, error 6.32606941826\n",
      "INFO:root:Iteration 139, error 6.04946611373\n",
      "INFO:root:Iteration 140, error 5.78535674038\n",
      "INFO:root:Iteration 141, error 5.53316346616\n",
      "INFO:root:Iteration 142, error 5.29233571638\n",
      "INFO:root:Iteration 143, error 5.06234886672\n",
      "INFO:root:Iteration 144, error 4.84270299982\n",
      "INFO:root:Iteration 145, error 4.63292172211\n",
      "INFO:root:Iteration 146, error 4.43255103822\n",
      "INFO:root:Iteration 147, error 4.24115827991\n",
      "INFO:root:Iteration 148, error 4.05833108693\n",
      "INFO:root:Iteration 149, error 3.88367643734\n",
      "INFO:root:Iteration 150, error 3.71681972468\n",
      "INFO:root:Iteration 151, error 3.55740387993\n",
      "INFO:root:Iteration 152, error 3.4050885358\n",
      "INFO:root:Iteration 153, error 3.25954923157\n",
      "INFO:root:Iteration 154, error 3.12047665623\n",
      "INFO:root:Iteration 155, error 2.98757592824\n",
      "INFO:root:Iteration 156, error 2.86056591002\n",
      "INFO:root:Iteration 157, error 2.73917855548\n",
      "INFO:root:Iteration 158, error 2.62315828904\n",
      "INFO:root:Iteration 159, error 2.51226141453\n",
      "INFO:root:Iteration 160, error 2.40625555253\n",
      "INFO:root:Iteration 161, error 2.30491910485\n",
      "INFO:root:Iteration 162, error 2.20804074465\n",
      "INFO:root:Iteration 163, error 2.11541893115\n",
      "INFO:root:Iteration 164, error 2.02686144757\n",
      "INFO:root:Iteration 165, error 1.94218496128\n",
      "INFO:root:Iteration 166, error 1.86121460505\n",
      "INFO:root:Iteration 167, error 1.7837835783\n",
      "INFO:root:Iteration 168, error 1.70973276755\n",
      "INFO:root:Iteration 169, error 1.63891038488\n",
      "INFO:root:Iteration 170, error 1.57117162387\n",
      "INFO:root:Iteration 171, error 1.5063783318\n",
      "INFO:root:Iteration 172, error 1.44439869762\n",
      "INFO:root:Iteration 173, error 1.38510695473\n",
      "INFO:root:Iteration 174, error 1.32838309804\n",
      "INFO:root:Iteration 175, error 1.27411261436\n",
      "INFO:root:Iteration 176, error 1.22218622577\n",
      "INFO:root:Iteration 177, error 1.17249964512\n",
      "INFO:root:Iteration 178, error 1.12495334315\n",
      "INFO:root:Iteration 179, error 1.07945232675\n",
      "INFO:root:Iteration 180, error 1.03590592765\n",
      "INFO:root:Iteration 181, error 0.994227601185\n",
      "INFO:root:Iteration 182, error 0.954334734604\n",
      "INFO:root:Iteration 183, error 0.916148464438\n",
      "INFO:root:Iteration 184, error 0.879593502515\n",
      "INFO:root:Iteration 185, error 0.844597970208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 186, error 0.811093240516\n",
      "INFO:root:Iteration 187, error 0.779013787594\n",
      "INFO:root:Iteration 188, error 0.748297043386\n",
      "INFO:root:Iteration 189, error 0.718883261016\n",
      "INFO:root:Iteration 190, error 0.690715384614\n",
      "INFO:root:Iteration 191, error 0.663738925267\n",
      "INFO:root:Iteration 192, error 0.6379018428\n",
      "INFO:root:Iteration 193, error 0.613154433119\n",
      "INFO:root:Iteration 194, error 0.589449220835\n",
      "INFO:root:Iteration 195, error 0.566740856928\n",
      "INFO:root:Iteration 196, error 0.544986021209\n",
      "INFO:root:Iteration 197, error 0.524143329342\n",
      "INFO:root:Iteration 198, error 0.504173244225\n",
      "INFO:root:Iteration 199, error 0.485037991506\n",
      "INFO:root:Iteration 200, error 0.466701479051\n",
      "INFO:root:Iteration 201, error 0.449129220161\n",
      "INFO:root:Iteration 202, error 0.432288260378\n",
      "INFO:root:Iteration 203, error 0.416147107697\n",
      "INFO:root:Iteration 204, error 0.400675666022\n",
      "INFO:root:Iteration 205, error 0.38584517172\n",
      "INFO:root:Iteration 206, error 0.371628133123\n",
      "INFO:root:Iteration 207, error 0.357998272832\n",
      "INFO:root:Iteration 208, error 0.344930472706\n",
      "INFO:root:Iteration 209, error 0.332400721388\n",
      "INFO:root:Iteration 210, error 0.320386064268\n",
      "INFO:root:Iteration 211, error 0.308864555755\n",
      "INFO:root:Iteration 212, error 0.297815213758\n",
      "INFO:root:Iteration 213, error 0.287217976262\n",
      "INFO:root:Iteration 214, error 0.277053659911\n",
      "INFO:root:Iteration 215, error 0.267303920499\n",
      "INFO:root:Iteration 216, error 0.257951215271\n",
      "INFO:root:Iteration 217, error 0.24897876697\n",
      "INFO:root:Iteration 218, error 0.240370529526\n",
      "INFO:root:Iteration 219, error 0.232111155319\n",
      "INFO:root:Iteration 220, error 0.22418596395\n",
      "INFO:root:Iteration 221, error 0.216580912432\n",
      "INFO:root:Iteration 222, error 0.209282566757\n",
      "INFO:root:Iteration 223, error 0.202278074749\n",
      "INFO:root:Iteration 224, error 0.195555140164\n",
      "INFO:root:Iteration 225, error 0.189101997972\n",
      "INFO:root:Iteration 226, error 0.182907390764\n",
      "INFO:root:Iteration 227, error 0.176960546227\n",
      "INFO:root:Iteration 228, error 0.171251155653\n",
      "INFO:root:Iteration 229, error 0.165769353415\n",
      "INFO:root:Iteration 230, error 0.160505697385\n",
      "INFO:root:Iteration 231, error 0.155451150223\n",
      "INFO:root:Iteration 232, error 0.150597061532\n",
      "INFO:root:Iteration 233, error 0.145935150809\n",
      "INFO:root:Iteration 234, error 0.141457491165\n",
      "INFO:root:Iteration 235, error 0.137156493788\n",
      "INFO:root:Iteration 236, error 0.133024893097\n",
      "INFO:root:Iteration 237, error 0.129055732576\n",
      "INFO:root:Iteration 238, error 0.125242351233\n",
      "INFO:root:Iteration 239, error 0.121578370674\n",
      "INFO:root:Iteration 240, error 0.11805768276\n",
      "INFO:root:Iteration 241, error 0.114674437812\n",
      "INFO:root:Iteration 242, error 0.111423033343\n",
      "INFO:root:Iteration 243, error 0.108298103306\n",
      "INFO:root:Iteration 244, error 0.105294507803\n",
      "INFO:root:Iteration 245, error 0.102407323274\n",
      "INFO:root:Iteration 246, error 0.0996318331084\n",
      "INFO:root:Iteration 247, error 0.096963518683\n",
      "INFO:root:Iteration 248, error 0.0943980507942\n",
      "INFO:root:Iteration 249, error 0.0919312814736\n",
      "INFO:root:Iteration 250, error 0.0895592361656\n",
      "INFO:root:Iteration 251, error 0.0872781062531\n",
      "INFO:root:Iteration 252, error 0.0850842419124\n",
      "INFO:root:Iteration 253, error 0.0829741452864\n",
      "INFO:root:Iteration 254, error 0.0809444639575\n",
      "INFO:root:Iteration 255, error 0.07899198471\n",
      "INFO:root:Iteration 256, error 0.077113627567\n",
      "INFO:root:Iteration 257, error 0.0753064400895\n",
      "INFO:root:Iteration 258, error 0.0735675919272\n",
      "INFO:root:Iteration 259, error 0.0718943696079\n",
      "INFO:root:Iteration 260, error 0.0702841715562\n",
      "INFO:root:Iteration 261, error 0.0687345033297\n",
      "INFO:root:Iteration 262, error 0.0672429730647\n",
      "INFO:root:Iteration 263, error 0.0658072871207\n",
      "INFO:root:Iteration 264, error 0.0644252459148\n",
      "INFO:root:Iteration 265, error 0.0630947399384\n",
      "INFO:root:Iteration 266, error 0.0618137459471\n",
      "INFO:root:Iteration 267, error 0.0605803233166\n",
      "INFO:root:Iteration 268, error 0.0593926105571\n",
      "INFO:root:Iteration 269, error 0.0582488219786\n",
      "INFO:root:Iteration 270, error 0.0571472445015\n",
      "INFO:root:Iteration 271, error 0.0560862346046\n",
      "INFO:root:Iteration 272, error 0.0550642154059\n",
      "INFO:root:Iteration 273, error 0.0540796738692\n",
      "INFO:root:Iteration 274, error 0.0531311581309\n",
      "INFO:root:Iteration 275, error 0.0522172749426\n",
      "INFO:root:Iteration 276, error 0.0513366872237\n",
      "INFO:root:Iteration 277, error 0.0504881117189\n",
      "INFO:root:Iteration 278, error 0.0496703167568\n",
      "INFO:root:Iteration 279, error 0.0488821201043\n",
      "INFO:root:Iteration 280, error 0.048122386913\n",
      "INFO:root:Iteration 281, error 0.0473900277536\n",
      "INFO:root:Iteration 282, error 0.0466839967344\n",
      "INFO:root:Iteration 283, error 0.0460032896996\n",
      "INFO:root:Iteration 284, error 0.0453469425051\n",
      "INFO:root:Iteration 285, error 0.0447140293671\n",
      "INFO:root:Iteration 286, error 0.0441036612819\n",
      "INFO:root:Iteration 287, error 0.0435149845112\n",
      "INFO:root:Iteration 288, error 0.0429471791335\n",
      "INFO:root:Iteration 289, error 0.0423994576556\n",
      "INFO:root:Iteration 290, error 0.0418710636834\n",
      "INFO:root:Iteration 291, error 0.0413612706488\n",
      "INFO:root:Iteration 292, error 0.0408693805901\n",
      "INFO:root:Iteration 293, error 0.0403947229844\n",
      "INFO:root:Iteration 294, error 0.0399366536287\n",
      "INFO:root:Iteration 295, error 0.0394945535681\n",
      "INFO:root:Iteration 296, error 0.039067828069\n",
      "INFO:root:Iteration 297, error 0.0386559056357\n",
      "INFO:root:Iteration 298, error 0.0382582370681\n",
      "INFO:root:Iteration 299, error 0.0378742945584\n",
      "INFO:root:Iteration 300, error 0.0375035708259\n",
      "INFO:root:Iteration 301, error 0.0371455782883\n",
      "INFO:root:Iteration 302, error 0.0367998482668\n",
      "INFO:root:Iteration 303, error 0.0364659302246\n",
      "INFO:root:Iteration 304, error 0.0361433910375\n",
      "INFO:root:Iteration 305, error 0.035831814294\n",
      "INFO:root:Iteration 306, error 0.0355307996253\n",
      "INFO:root:Iteration 307, error 0.035239962062\n",
      "INFO:root:Iteration 308, error 0.0349589314188\n",
      "INFO:root:Iteration 309, error 0.0346873517032\n",
      "INFO:root:Iteration 310, error 0.0344248805496\n",
      "INFO:root:Iteration 311, error 0.0341711886763\n",
      "INFO:root:Iteration 312, error 0.0339259593651\n",
      "INFO:root:Iteration 313, error 0.0336888879619\n",
      "INFO:root:Iteration 314, error 0.0334596813983\n",
      "INFO:root:Iteration 315, error 0.0332380577323\n",
      "INFO:root:Iteration 316, error 0.0330237457086\n",
      "INFO:root:Iteration 317, error 0.0328164843356\n",
      "INFO:root:Iteration 318, error 0.0326160224813\n",
      "INFO:root:Iteration 319, error 0.0324221184842\n",
      "INFO:root:Iteration 320, error 0.0322345397812\n",
      "INFO:root:Iteration 321, error 0.0320530625497\n",
      "INFO:root:Iteration 322, error 0.0318774713652\n",
      "INFO:root:Iteration 323, error 0.0317075588719\n",
      "INFO:root:Iteration 324, error 0.031543125467\n",
      "INFO:root:Iteration 325, error 0.0313839789985\n",
      "INFO:root:Iteration 326, error 0.0312299344738\n",
      "INFO:root:Iteration 327, error 0.0310808137812\n",
      "INFO:root:Iteration 328, error 0.0309364454223\n",
      "INFO:root:Iteration 329, error 0.0307966642549\n",
      "INFO:root:Iteration 330, error 0.030661311247\n",
      "INFO:root:Iteration 331, error 0.0305302332395\n",
      "INFO:root:Iteration 332, error 0.0304032827196\n",
      "INFO:root:Iteration 333, error 0.0302803176027\n",
      "INFO:root:Iteration 334, error 0.0301612010229\n",
      "INFO:root:Iteration 335, error 0.0300458011323\n",
      "INFO:root:Iteration 336, error 0.0299339909082\n",
      "INFO:root:Iteration 337, error 0.0298256479674\n",
      "INFO:root:Iteration 338, error 0.0297206543891\n",
      "INFO:root:Iteration 339, error 0.0296188965433\n",
      "INFO:root:Iteration 340, error 0.0295202649277\n",
      "INFO:root:Convergence has reached.\n",
      "INFO:root: Theta: [  0.53214566  19.59570789  58.99543373]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('regression mse', 0.029520264927707082)\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression(lr=0.01, max_iters=200000, penalty='l2', C=0.03)\n",
    "model.fit(X, y)\n",
    "y_predict = model.predict(X)\n",
    "print('regression mse', mean_squared_error(y, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adapted from http://stackoverflow.com/a/25628397/200764\n",
    "# see also http://matplotlib.org/users/colormaps.html\n",
    "import matplotlib.cm as cmx\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "def get_cmap(N):\n",
    "    '''Returns a function that maps each index in 0, 1, ... N-1 to a distinct \n",
    "    RGB color.'''\n",
    "    color_norm  = colors.Normalize(vmin=0, vmax=N-1)\n",
    "    scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='rainbow') \n",
    "    def map_index_to_rgb_color(index):\n",
    "        return scalar_map.to_rgba(index)\n",
    "    return map_index_to_rgb_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAABECAYAAABd9/ShAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f001c137490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "\n",
    "def cycolor(N):\n",
    "    cmap = get_cmap(N)\n",
    "    i = 0\n",
    "    while True:\n",
    "        index = i\n",
    "        yield cmap(index)\n",
    "        i = (i + 3) % N\n",
    "\n",
    "N = 10\n",
    "cycol=cycolor(N).next\n",
    "        \n",
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(111)   \n",
    "plt.axis('scaled')\n",
    "ax.set_xlim([ 0, N])\n",
    "ax.set_ylim([-0.5, 0.5])\n",
    "for i in xrange(N):\n",
    "    col = cycol()\n",
    "    rect = plt.Rectangle((i, -0.5), 1, 1, facecolor=col)\n",
    "    ax.add_artist(rect)\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f00158fbf50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0034a0e5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for i in xrange(len(X_trans)):\n",
    "    ax.scatter(X_trans[i], y, c=cycol(), label='y%d' % i)\n",
    "    ax.scatter(X_trans[i], y_predict, c=cycol(), label='y_predict%d' % i)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False, False,  True, False,\n",
       "        True,  True,  True, False, False,  True,  True,  True,  True,\n",
       "       False,  True,  True,  True,  True, False,  True, False,  True,\n",
       "       False,  True,  True,  True,  True,  True,  True,  True, False,\n",
       "       False,  True,  True,  True, False,  True,  True,  True, False,\n",
       "       False, False,  True,  True, False,  True,  True,  True,  True,\n",
       "        True, False,  True,  True, False,  True,  True, False,  True,\n",
       "        True, False, False,  True, False,  True,  True,  True,  True,\n",
       "        True,  True,  True, False,  True, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True, False,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_close = np.isclose(y, y_predict, rtol=0.005)\n",
    "not_close = np.logical_not(is_close)\n",
    "is_close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nbuser/anaconda2_501/lib/python2.7/site-packages/matplotlib/collections.py:836: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f00137d4450>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3EIIkWEkcQshRIb5/5oxynpXQAh1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f001c12d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(X_trans[0][is_close], X_trans[1][is_close], c=cycol(), s=y[is_close], label='y is close')\n",
    "ax.scatter(X_trans[0][not_close], X_trans[1][not_close], c=cycol(), s=y[not_close], label='y not close')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(100,), (100,), (100,), (100,)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAHICAYAAABd6mKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfMgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0015828450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.gca(projection='3d')\n",
    "ax.scatter(X_trans[0], X_trans[1], y, label='y_train', marker='^', c=cycol())\n",
    "ax.scatter(X_trans[0], X_trans[1], y_predict - 20, label='y_predict', marker='o', c=cycol())\n",
    "ax.legend()\n",
    "[X_trans[0].shape, X_trans[1].shape, y.shape, y_predict.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classification():\n",
    "    # Generate a random binary classification problem.\n",
    "    X, y = make_classification(n_samples=1000, n_features=100,\n",
    "                               n_informative=75, random_state=1111,\n",
    "                               n_classes=2, class_sep=2.5, )\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,\n",
    "                                                        random_state=1111)\n",
    "\n",
    "    model = LogisticRegression(lr=0.01, max_iters=500, penalty='l1', C=0.01)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    print('classification accuracy', accuracy(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 1, error 9809.11644382\n",
      "/home/nbuser/anaconda2_501/lib/python2.7/site-packages/autograd/tracer.py:48: RuntimeWarning: overflow encountered in cosh\n",
      "  return f_raw(*args, **kwargs)\n",
      "/home/nbuser/anaconda2_501/lib/python2.7/site-packages/autograd/numpy/numpy_vjps.py:88: RuntimeWarning: overflow encountered in square\n",
      "  defvjp(anp.tanh,   lambda ans, x : lambda g: g / anp.cosh(x) **2)\n",
      "INFO:root:Iteration 2, error 9226.00393327\n",
      "INFO:root:Iteration 3, error 8427.55499326\n",
      "INFO:root:Iteration 4, error 7979.54730174\n",
      "INFO:root:Iteration 5, error 7425.92568022\n",
      "INFO:root:Iteration 6, error 7115.06869669\n",
      "INFO:root:Iteration 7, error 6804.21731035\n",
      "INFO:root:Iteration 8, error 6286.72645802\n",
      "INFO:root:Iteration 9, error 5906.20912408\n",
      "INFO:root:Iteration 10, error 4835.49346268\n",
      "INFO:root:Iteration 11, error 3628.17651508\n",
      "INFO:root:Iteration 12, error 2866.75762105\n",
      "INFO:root:Iteration 13, error 1381.56384936\n",
      "INFO:root:Iteration 14, error 1554.26332851\n",
      "INFO:root:Iteration 15, error 1105.2528386\n",
      "INFO:root:Iteration 16, error 1001.75880264\n",
      "INFO:root:Iteration 17, error 932.554159039\n",
      "INFO:root:Iteration 18, error 829.752055014\n",
      "INFO:root:Iteration 19, error 690.783523873\n",
      "INFO:root:Iteration 20, error 690.783523873\n",
      "INFO:root:Convergence has reached.\n",
      "INFO:root: Theta: [  0.23566603   2.7296357    4.7956022   -4.83958473   0.27545487\n",
      "  -0.78056608  -1.77853487  -6.12823039  -2.30815794  -2.0842781\n",
      "  -0.33172152  11.11842747 -14.57466373  -7.05675679   2.16486984\n",
      "   1.50808307   6.35661816   4.84312088  -5.82688182   1.3400126\n",
      "   0.65108352   4.68269614   1.37689522   0.75641437  12.00077792\n",
      "  -5.85743875   1.37131971   0.76771866   0.57164638  -6.75591811\n",
      " -12.67298471  -3.96918584   6.01852644   8.36629321  -4.76625949\n",
      "   1.36312208  -6.51647839 -10.89929268  -1.87797361   1.46375967\n",
      "  -2.55894534   0.26119161   1.01873334   0.98119042  -7.79066367\n",
      "   3.74242474   1.84371356  -2.4005536   -0.28833902  -0.53784624\n",
      "  -6.76080894  -4.75387683   0.14916229  10.36742357  -7.13843422\n",
      "  -0.49636221  -3.85431437   4.80025972   5.78652109  -6.9147896\n",
      "  -0.76397065  -1.02926599   0.57391646  -1.13822616  -0.46362281\n",
      "  -2.11436143  -1.02450423   4.48944062  -1.54137654   3.68867641\n",
      "   1.14488344  -0.55546238  -0.07995588   3.85883021   0.45481992\n",
      "   1.37244476  -4.47366316   5.24318539   0.6642306  -13.09959962\n",
      "  -4.90233945   0.02058884  10.11790232  -0.79230859  -0.49292453\n",
      " -11.45910867   6.54695195   0.2455275    0.15937865 -12.17170847\n",
      "  -0.03284348   4.24712329   2.62011597  -0.2644913    0.39089953\n",
      "   0.85325067   7.45580962   7.8972504  -11.64427748   6.5212611\n",
      "  -2.85115954]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('classification accuracy', 0.97999999999999998)\n"
     ]
    }
   ],
   "source": [
    "classification()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=2,\n",
    "                               n_informative=2, n_redundant=0, random_state=1111,\n",
    "                               n_classes=2, class_sep=2.5, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 1000), (1000,)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans = np.transpose(X)\n",
    "[X_trans.shape, y.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 1, error 241.776232349\n",
      "INFO:root: Theta: [  0.42613801  -5.98440447  42.46385047]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('classification accuracy', 0.99099999999999999)\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(lr=0.01, max_iters=1, penalty='l1', C=0.01)\n",
    "model.fit(X, y)\n",
    "y_predict = model.predict(X)\n",
    "print('classification accuracy', accuracy(y, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "is_close = np.isclose(y, y_predict)\n",
    "not_close = np.logical_not(is_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0013687610>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00136cf150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(X_trans[0][is_close], X_trans[1][is_close], c=cycol(), s=(y[is_close] + 0.1) * 100, label='y is close')\n",
    "ax.scatter(X_trans[0][not_close], X_trans[1][not_close], c=cycol(), s=(y[not_close] + 0.1) * 100, label='y not close')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1000,), (1000,), (1000,), (1000,)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAHICAYAAABd6mKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00136db910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.gca(projection='3d')\n",
    "# ax.scatter(X_trans[0], X_trans[1], y, label='y_train', marker='^', c=cycol())\n",
    "ax.scatter(X_trans[0][is_close], X_trans[1][is_close], y_predict[is_close], label='correct y_predict', marker='o', c=cycol())\n",
    "ax.scatter(X_trans[0][not_close], X_trans[1][not_close], y_predict[not_close], label='not close y_predict', marker='o', c=cycol())\n",
    "ax.legend()\n",
    "[X_trans[0].shape, X_trans[1].shape, y.shape, y_predict.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=2,\n",
    "                               n_informative=2, n_redundant=0, random_state=1111,\n",
    "                               n_classes=2, class_sep=2.5, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 1000), (1000,)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans = np.transpose(X)\n",
    "[X_trans.shape, y.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Iteration 1, error 8293.28789044\n",
      "INFO:root:Iteration 2, error 4528.64678798\n",
      "INFO:root:Iteration 3, error 2489.2515916\n",
      "INFO:root:Iteration 4, error 1314.79586718\n",
      "INFO:root:Iteration 5, error 900.203190076\n",
      "INFO:root:Iteration 6, error 587.168793883\n",
      "INFO:root:Iteration 7, error 518.222997405\n",
      "INFO:root:Iteration 8, error 483.550865503\n",
      "INFO:root:Iteration 9, error 483.550865503\n",
      "INFO:root:Convergence has reached.\n",
      "INFO:root: Theta: [  0.11701526   6.74515699  33.81095895]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('classification accuracy', 0.96499999999999997)\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(lr=0.01, max_iters=1000, penalty='l1', C=0.01)\n",
    "model.fit(X, y)\n",
    "y_predict = model.predict(X)\n",
    "print('classification accuracy', accuracy(y, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "is_close = np.isclose(y, y_predict)\n",
    "not_close = np.logical_not(is_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0013486710>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0013611a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(X_trans[0][is_close], X_trans[1][is_close], c=cycol(), s=(y[is_close] + 0.1) * 100, label='y is close')\n",
    "ax.scatter(X_trans[0][not_close], X_trans[1][not_close], c=cycol(), s=(y[not_close] + 0.1) * 100, label='y not close')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1000,), (1000,), (1000,), (1000,)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAHICAYAAABd6mKEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f00134cd790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.gca(projection='3d')\n",
    "# ax.scatter(X_trans[0], X_trans[1], y, label='y_train', marker='^', c=cycol())\n",
    "ax.scatter(X_trans[0][is_close], X_trans[1][is_close], y_predict[is_close], label='correct y_predict', marker='o', c=cycol())\n",
    "ax.scatter(X_trans[0][not_close], X_trans[1][not_close], y_predict[not_close], label='not close y_predict', marker='o', c=cycol())\n",
    "ax.legend()\n",
    "[X_trans[0].shape, X_trans[1].shape, y.shape, y_predict.shape]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
